David Z. Pan (IEEE Fellow, SPIE Fellow) received his BS degree from Peking University and MS/PhD degrees from UCLA. He is currently Engineering Foundation Professor at the Department of Electrical and Computer Engineering, The University of Texas at Austin.His research interests include cross-layer IC design for manufacturing, reliability, security, machine learning in EDA, hardware acceleration,design/CAD for analog/mixed signal designs and emerging technologies such as nanophotonics. He has published over 300 refereed journal/conference papers and 8 US patents. He has served in many journal editorial boards and conference committees, including various leadership roles. He has received many prestigious awards, including SRC Technical Excellence Award, 15 Best Paper Awards at premier venues, DAC Top 10 Author Award in Fifth Decade, ASP-DAC Frequently Cited Author Award, Communications of ACM Research Highlights, ACM/SIGDA Outstanding New Faculty Award, NSF CAREER Award, SRC Inventor Recognition Award (3 times), IBM Faculty Award (4 times), UCLA Engineering Distinguished Young Alumnus Award, UT Austin RAISE Faculty Excellence Award, many international CAD contest awards, among others. He has graduated 23 PhD students who have won many awards, including First Place of ACM Student Research Competition Grand Finals, ACM/SIGDA Student Research Competition Gold Medal (twice), ACM Outstanding PhD Dissertation in EDA (twice).

报告摘要:

Artificial intelligence (AI) studies the theory and development of computing systems able to learn, reason, act and adapt. Integrated circuit (ICs), powered by the semiconductor technology, enable all modern computing systems with amazing level of integration, e.g., a small chip (<1cm2) nowadays can integrate billions of transistors. As the semiconductor technology enters the era of extreme scaling (1x nm), the IC design and manufacturing complexities are extremely high. Intelligent cross-layer design and manufacturing co-optimizations are in critical demand for better performance, power, yield, reliability, security, time-to-market, and so on. This talk will discuss the synergistic link between modern AI technologies (e.g., pattern recognition, machine learning, deep learning) with intelligent deep-nanoscale IC design and manufacturing. Several case studies will be presented, including advanced lithography modeling, hotspot detection, mask synthesis, and physical design. Customized AI chips can further improve the training and inference performance-energy efficiency by orders of magnitude. Thus, the co-evolution of AI algorithms and IC technologies shall be investigated jointly to enhance the research and development of each other.